We prove that the Hilbert scheme of
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group
 NSFPAR ID:
 10421297
 Publisher / Repository:
 Springer Science + Business Media
 Date Published:
 Journal Name:
 The European Physical Journal C
 Volume:
 83
 Issue:
 6
 ISSN:
 14346052
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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